A kernel is a function used as a basis to model an underlying probability density. In existing kernel estimators used in pattern classification, a Gaussian kernel based on the function e.sup.-x/2 is used. However, problems arise when a probability density of a feature space to be classified contains a jump discontinuity, as indicated generally by reference numeral 10 in the graph of FIG. 1. (A jump discontinuity can be thought of in this way. If a shipboard infrared sensor was scanning the horizon in a circular manner and encountered a sun glint off the water, a plot of the intensity distribution would yield a jump discontinuity at the sun glint.) The jump discontinuity poses a problem particularly if a target to be classified is near the jump discontinuity. If the Gaussian kernel estimator's window is set too wide, jump discontinuity 10 will likely be "smoothed" by a Gaussian kernel estimator. However, the target may be lost in the smoothing operation. On the other hand, if the Gaussian kernel estimator's window is set narrowly to detect jump discontinuity 10, the output of the estimator may be too noisy to detect and classify the target. Thus, the Gaussian kernel estimator cannot accurately be used in classification schemes where jump discontinuities are probable.